Scholarlink

<p>Dual antiplatelet therapy (DAPT) following percutaneous coronary intervention (PCI) is traditionally guided by rule-based scores providing static, single-time-point or fixed time-interval estimates with modest discrimination (C-index 0.63–0.73), limiting individualized DAPT duration decisions. We developed Transformer-DAPT, a transformer-based deep learning survival framework designed to estimate patient-specific ischemic and bleeding risks across clinically relevant time intervals during the first year after PCI. Using electronic health records from 29,032 patients at Mayo Clinic and externally validated in 19,173 patients from the OneFlorida+ Clinical Research Consortium, Transformer-DAPT achieved time-dependent concordance indices (<i>C</i><sup>td</sup>) of 0.84–0.87 for ischemic events and 0.81–0.88 for bleeding events, outperforming DeepSurv and DeepHit by 2%–12%. In the external cohort, <i>C</i><sup>td</sup> ranged from 0.74–0.84 and 0.75–0.83 for ischemic and bleeding events, respectively. Transformer-DAPT demonstrated improved discrimination and calibration performance, providing a framework for multi-interval risk prediction to support personalized DAPT management after PCI.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Transformer-DAPT: AI-based dynamic assessment of ischemic and bleeding risks in patients on DAPT following PCI

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月14日

查看全文
  • Ahmed Abdelhameed,
  • Zenan Sun,
  • Yue Yu,
  • Xinyue Hu,
  • Demilade Adedinsewo,
  • Jianping He,
  • Yifang Dang,
  • Jianfu Li,
  • Jingna Feng,
  • Haifang Li,
  • Laila Rasmy,
  • Michael Gharacholou,
  • Degui Zhi,
  • Jiang Bian,
  • Fang Li,
  • Cui Tao

摘要

Dual antiplatelet therapy (DAPT) following percutaneous coronary intervention (PCI) is traditionally guided by rule-based scores providing static, single-time-point or fixed time-interval estimates with modest discrimination (C-index 0.63–0.73), limiting individualized DAPT duration decisions. We developed Transformer-DAPT, a transformer-based deep learning survival framework designed to estimate patient-specific ischemic and bleeding risks across clinically relevant time intervals during the first year after PCI. Using electronic health records from 29,032 patients at Mayo Clinic and externally validated in 19,173 patients from the OneFlorida+ Clinical Research Consortium, Transformer-DAPT achieved time-dependent concordance indices (Ctd) of 0.84–0.87 for ischemic events and 0.81–0.88 for bleeding events, outperforming DeepSurv and DeepHit by 2%–12%. In the external cohort, Ctd ranged from 0.74–0.84 and 0.75–0.83 for ischemic and bleeding events, respectively. Transformer-DAPT demonstrated improved discrimination and calibration performance, providing a framework for multi-interval risk prediction to support personalized DAPT management after PCI.